ColossalAI/colossalai/nn/layer/base_layer.py

64 lines
2.8 KiB
Python

#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from contextlib import contextmanager
import torch.nn as nn
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
class ParallelLayer(nn.Module):
global_state_dict: bool = True
def __init__(self):
super().__init__()
self.data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(
ParallelMode.DATA)
self.data_parallel_size = 1 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_world_size(
ParallelMode.DATA)
self.tensor_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_local_rank(
ParallelMode.TENSOR)
self.tensor_parallel_size = 1 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_world_size(
ParallelMode.TENSOR)
self.pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
ParallelMode.PIPELINE)
self.pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
ParallelMode.PIPELINE)
def _load_from_global_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs):
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs)
def _save_to_global_state_dict(self, destination, prefix, keep_vars):
return super()._save_to_state_dict(destination, prefix, keep_vars)
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs):
if self.global_state_dict:
if gpc.get_local_rank(ParallelMode.TENSOR) != 0:
missing_keys.clear()
unexpected_keys.clear()
return self._load_from_global_state_dict(state_dict, prefix, local_metadata, strict, missing_keys,
unexpected_keys, error_msgs)
return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs)
def _save_to_state_dict(self, destination, prefix, keep_vars):
if self.global_state_dict:
return self._save_to_global_state_dict(destination, prefix, keep_vars)
return super()._save_to_state_dict(destination, prefix, keep_vars)
@classmethod
@contextmanager
def use_local_state_dict(cls):
try:
cls.global_state_dict = False
yield
finally:
cls.global_state_dict = True